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Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research.  ...  Deep learning has become the most widely used approach for cardiac image segmentation in recent years.  ...  ACKNOWLEDGMENTS We would like to thank our colleagues: Karl Hahn, Qingjie Meng, James Batten, and Jonathan Passerat-Palmbach who provided the insight and expertise that greatly assisted the work, and also  ... 
doi:10.3389/fcvm.2020.00025 pmid:32195270 pmcid:PMC7066212 fatcat:iw7xpnltn5cgbn5ullq2ldy3nq

Learning To Score Olympic Events [article]

Paritosh Parmar, Brendan Tran Morris
2017 arXiv   pre-print
The proposed systems show significant improvement over existing quality assessment approaches on the task of predicting scores of Olympic events diving, vault, figure skating.  ...  While the SVR-based frameworks yield better results, LSTM-based frameworks are more natural for describing an action and can be used for improvement feedback.  ...  LSTM Final-Label Training The problem of action quality assessment is at heart a manyto-one mapping problem, because, given a stack of frames (or equivalently clips), we want to predict a score (either  ... 
arXiv:1611.05125v3 fatcat:pe7qbagenvdtxbpe5olk6mxmqa

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis [article]

Stefanos Angelidis, Mirella Lapata
2018 arXiv   pre-print
We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for  ...  We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).  ...  We thank TACL action editor Ani Nenkova and the anonymous reviewers whose feedback helped improve the present paper, as well as Charles Sutton, Timothy Hospedales, and members of EdinburghNLP for helpful  ... 
arXiv:1711.09645v2 fatcat:rl6h7n7werg5pgq3cbhkeykewq

Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

Stefanos Angelidis, Mirella Lapata
2018 Transactions of the Association for Computational Linguistics  
We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for  ...  We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL).  ...  We thank TACL action editor Ani Nenkova and the anonymous reviewers whose feedback helped improve the present paper, as well as Charles Sutton, Timothy Hospedales, and members of EdinburghNLP for helpful  ... 
doi:10.1162/tacl_a_00002 fatcat:ax5kmuxujjdpvjpijubm5zr4li

RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging [article]

Ali Hatamizadeh, Hamid Hosseini, Niraj Patel, Jinseo Choi, Cameron C. Pole, Cory M. Hoeferlin, Steven D. Schwartz, Demetri Terzopoulos
2022 arXiv   pre-print
We propose a novel deep learning-based methodology, denoted as SegRAVIR, for the semantic segmentation of retinal arteries and veins and the quantitative measurement of the widths of segmented vessels.  ...  The objective assessment of retinal vessels has long been considered a surrogate biomarker for systemic vascular diseases, and with recent advancements in retinal imaging and computer vision technologies  ...  . 2) We propose a novel framework with tailored loss functions for the segmentation task.  ... 
arXiv:2203.14928v1 fatcat:z2vlagfoubayzpu4xztbjj3gki

Event perception: A mind-brain perspective

Jeffrey M. Zacks, Nicole K. Speer, Khena M. Swallow, Todd S. Braver, Jeremy R. Reynolds
2007 Psychological bulletin  
Perceptual systems continuously make predictions about what will happen next. When transient errors in predictions arise, an event boundary is perceived.  ...  Neurological and neurophysiological data suggest that representations of events may be implemented by structures in the lateral prefrontal cortex and that perceptual prediction error is calculated and  ...  If viewers segmenting at a fine grain were spontaneously grouping fine-grained events into larger units, one would expect coarse-grained event boundaries to be a subset of fine-grained event boundaries  ... 
doi:10.1037/0033-2909.133.2.273 pmid:17338600 pmcid:PMC2852534 fatcat:5zrtd3tqvzeltilr7rucvwtviy

Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention.  ...  We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.  ...  [151] created a 3D FCN fractal network for whole heart and great vessel volume-to-volume segmentation.  ... 
doi:10.1109/rbme.2018.2885714 fatcat:pa47trmskvflvig5cotth265q4

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets.  ...  data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations.  ...  As a consequence, CRF is able to refine a collection of inaccurate and coarse pixel-level predictions, producing sharp boundaries and fine-grained segmentation masks.  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark [article]

Yunhe Gao, Mu Zhou, Di Liu, Zhennan Yan, Shaoting Zhang, Dimitris N. Metaxas
2022 arXiv   pre-print
We make the data processing, models and evaluation pipeline publicly available, offering solid baselines and unbiased comparisons for promoting a wide range of downstream clinical applications.  ...  Extensive experiments demonstrate the potential of UTNetV2 as a general segmentation backbone, outperforming CNNs and vision Transformers on three public datasets with multiple modalities (e.g., CT and  ...  In addition, feature extraction from coarse-level feature maps does not improve Transformer models on capturing fine-grained details, a critical ability needed for pixel-based segmentation.  ... 
arXiv:2203.00131v3 fatcat:dmuh4yga4rahzjjdy4ttg7eei4

Large-scale biometry with interpretable neural network regression on UK Biobank body MRI [article]

Taro Langner, Robin Strand, Håkan Ahlström, Joel Kullberg
2020 arXiv   pre-print
On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.  ...  With the ResNet50, the standardized framework achieves a close fit to the target values (median R^2 > 0.97) in cross-validation.  ...  Acknowledgements This work was supported by a research grant from the Swedish Heart-Lung Foundation and the Swedish Research Council (2016-01040, 2019-04756) and used the UK Biobank resource under application  ... 
arXiv:2002.06862v3 fatcat:cm6vk4rrtzg3vdl2cbklcjwi34

Generative Adversarial Network in Medical Imaging: A Review [article]

Xin Yi, Ekta Walia, Paul Babyn
2019 arXiv   pre-print
These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection  ...  The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency.  ...  Mok and Chung (2018) used cGAN to augment training images for brain tumour segmentation. The generator was conditioned on a segmentation map and generated brain MR images in a coarse to fine manner.  ... 
arXiv:1809.07294v3 fatcat:5j5i6shlcvbbjm74ceidzg6rc4

Deep learning in medical image registration

Xiang Chen, Andres Diaz-Pinto, Nishant Ravikumar, Alejandro Frangi
2020 Progress in Biomedical Engineering  
of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.  ...  Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary.  ...  Acknowledgments The Royal Academy of Engineering supports the work of A F F through a Chair in Emerging Technologies (CiET1819\19) and the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical  ... 
doi:10.1088/2516-1091/abd37c fatcat:74w7ra4f7nfrrpfk2ifvmijntq

Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation

Hugo Oliveira, Edemir Ferreira, Jefersson A. Dos Santos
2020 IEEE Access  
In this work, we propose a novel approach for segmentation of biomedical images based on Generative Adversarial Networks.  ...  The proposed method yielded consistently better results than the baselines in scarce labeled data scenarios, achieving Jaccard values greater than 0.9 and good segmentation quality in most tasks.  ...  ACKNOWLEDGMENT The authors would like to thank NVIDIA for the donation of the GPUs that allowed the execution of all experiments in this artice.  ... 
doi:10.1109/access.2020.2991688 fatcat:jqascltn7zft3a5g5tiagnjsgi

Multi-class probabilistic atlas-based whole heart segmentation method in cardiac CT and MRI

Tarun Kanti Ghosh, Md. Kamrul Hasan, Shidhartho Roy, Md. Ashraful Alam, Eklas Hossain, Mohiuddin Ahmad
2021 IEEE Access  
An UNet-based Omega-Net was introduced in [18] consisting of a set of UNet for fine-grained WHS.  ...  a framework consisting of two 3D-UNets, where the first network was employed to localize the bounding box encompassing the heart, and the second network was used for the fine segmentation of different  ...  This work is licensed under a Creative Commons Attribution 4.0 License.  ... 
doi:10.1109/access.2021.3077006 fatcat:omsuhvijijdk5bezk42pexu37q

Convolutional-neural-network-based Approach for Segmentation of Apical Four-chamber View from Fetal Echocardiography

Lu Xu, Mingyuan Liu, Jicong Zhang, Yihua He
2020 IEEE Access  
Third, the method leverages on SSIM loss to preserve fine-grained structural information and obtain clear boundaries.  ...  effectiveness and potential as a clinical tool.  ...  The SSIM, originally proposed for image quality assessment [24] , measures the similarity between two images.  ... 
doi:10.1109/access.2020.2984630 fatcat:j47pd47iafacbdnz4fgbonciou
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